title: Interrupted time-series analysis yielded an effect estimate concordant with the cluster-randomized controlled trial result.
abstract: OBJECTIVE
  We reanalyzed the data from a cluster-randomized controlled trial (C-RCT) of a quality improvement intervention for prescribing antihypertensive medication. Our objective was to estimate the effectiveness of the intervention using both interrupted time-series (ITS) and RCT methods, and to compare the findings.
  
  
  METHODS
  We first conducted an ITS analysis using data only from the intervention arm of the trial because our main objective was to compare the findings from an ITS analysis with the findings from the C-RCT. We used segmented regression methods to estimate changes in level or slope coincident with the intervention, controlling for baseline trend. We analyzed the C-RCT data using generalized estimating equations. Last, we estimated the intervention effect by including data from both study groups and by conducting a controlled ITS analysis of the difference between the slope and level changes in the intervention and control groups.
  
  
  RESULTS
  The estimates of absolute change resulting from the intervention were ITS analysis, 11.5% (95% confidence interval [CI]: 9.5, 13.5); C-RCT, 9.0% (95% CI: 4.9, 13.1); and the controlled ITS analysis, 14.0% (95% CI: 8.6, 19.4).
  
  
  CONCLUSIONS
  ITS analysis can provide an effect estimate that is concordant with the results of a cluster-randomized trial. A broader range of comparisons from other RCTs would help to determine whether these are generalizable results.
authors.affiliation: Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA 02215, USA. atle.fretheim@nokc.no
authors.forename: Atle
authors.initials: A
authors.lastname: Fretheim
authors.forename: Stephen B
authors.initials: SB
authors.lastname: Soumerai
authors.forename: Fang
authors.initials: F
authors.lastname: Zhang
authors.forename: Andrew D
authors.initials: AD
authors.lastname: Oxman
authors.forename: Dennis
authors.initials: D
authors.lastname: Ross-Degnan
mesh_terms: D000959:Antihypertensive Agents / Q000627:therapeutic use; D016000:Cluster Analysis; D003627:Data Interpretation, Statistical; D011307:Drug Prescriptions / Q000706:statistics & numerical data; D004363:Drug Utilization / Q000592:standards*; D005194:Family Practice / Q000193:education; D019983:Guideline Adherence / Q000706:statistics & numerical data*; D006302:Health Services Research / Q000706:statistics & numerical data*; D006801:Humans; D009664:Norway; D010818:Practice Patterns, Physicians' / Q000592:standards*; D058996:Quality Improvement; D012044:Regression Analysis; D013997:Time Factors
chemical_list: D000959:Antihypertensive Agents